Campaign and competitive analysis and data visualization based on search interest data

ABSTRACT

Techniques for providing interactive visualizations for an entity or group of entities based on search interest data are provided. The search interest data may be derived from Internet or online search data related to the entities and relevant attributes of the entities. Each of the entities and attributes may be represented in a structured search market using a predefined list of relevant search terms or keywords. The search interest of each entity and attribute may be determined based on probabilities representing a likelihood of a search for an entity co-occurring with a search for a relevant attribute within a predetermined time proximity.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority benefit under 35 U.S.C. §119(e)from U.S. Provisional Application No. 61/675,774, filed Jul. 25, 2012,which is incorporated herein by reference in its entirety.

BACKGROUND

The present disclosure relates generally to data visualization, and,more particularly, to visualization of search interest data associatedwith various entities.

Market research has been used by advertisers, marketers and other groupsto acquire more information about a particular market, market segment,or entity. For example, this research may be used to gauge consumerinterest in particular brands, and how such interest may vary over timeand across different geographic locations. However, conventionalsolutions for conducting research on consumer markets or product brandsgenerally involve using customer polls or surveys including, forexample, traditional surveys conducted on an ad hoc basis (e.g., viatelephone or forms mailed to physical home addresses) or online surveysvia web forms posted to web pages or an online discussion panel.However, such conventional solutions are expensive, time consuming andassume that the responses from customers provide an accurate reflectionof their actual interest in a brand or entity for a given market.Moreover, the results of these traditional market surveys or customerpolls are generally not available to the interested party (e.g., aparticular business enterprise) until well after the initial time periodwhen the research was conducted, and therefore, may be less relevantwith respect to the current time period.

SUMMARY

The disclosed subject matter relates to visualizing information relatedto entities in an industry or market segment based on user searchinterest data.

In an example method, search parameters are identified for defining astructured search market based on input from a user. The searchparameters include a first list of search terms representing one or moresearch entities and a second list of search terms representing one ormore attributes for the one or more search entities in the first list.Aggregated online search query data is processed based on the identifiedsearch parameters of the structured search market. An interactivevisualization of interrelations between the one or more search entitiesand the one or more attributes within the structured search market isprovided based on the processed search query data.

In another example method, a historic trend of online search interest ina search entity is determined based on search queries related to thesearch entity submitted by users to a search engine during a first timeperiod. The search entity corresponds to one or more related searchterms submitted as part of the related search queries. A current trendof online search interest in the search entity is then determined basedon search queries related to the search entity submitted during a secondtime period. An interactive visualization is provided, where thevisualization compares the historic and current search trends of onlinesearch interest for the search entity over a time series including thefirst and second time periods.

In yet another example method for providing a visualization of searchinterest data for entities in a market, a search frequency for eachentity in a list of entities in the market is determined based on anaggregate record of user search queries related to the entities within apredetermined time period. Each entity in the list of entities may berepresented by a predefined list of search terms associated with theentity. Search probabilities are calculated for each of the entitiesbased on the determined search frequency. The search probabilitiesindicate the likelihood of a search for each entity co-occurring witheach of the search terms in the corresponding list of search termsassociated with the entity. A user interest level for each of theentities is estimated based on the calculated search probabilities. Avisualization of the estimated user interest level for each of theentities in the list of entities is then presented.

It is understood that other configurations of the subject technologywill become readily apparent to those skilled in the art from thefollowing detailed description, wherein various configurations of thesubject technology are shown and described by way of illustration. Aswill be realized, the subject technology is capable of other anddifferent configurations and its several details are capable ofmodification in various other respects, all without departing from thescope of the subject technology. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the subject technology are set forth in theappended claims. However, for purpose of explanation, severalconfigurations of the subject technology are set forth in the followingfigures.

FIG. 1 is a diagram of an example network environment suitable forpracticing an implementation of the subject technology.

FIG. 2 illustrates a process flowchart of an example method for marketanalysis and visualization of information for different market entitiesrelated to a real-world market based on search interest data.

FIGS. 3A-3C illustrate an example interface for modeling a real-worldmarket based on search interest data in order to perform brand analysis.

FIGS. 4A-4D illustrate an example interactive visualization of aconceptual map of different brands in a market segment based on searchinterest data.

FIGS. 5A-5C illustrate an example visualization showing the distributionof search interest between different brands and one or more brandattributes in a market based on search interest data.

FIG. 6 illustrates an example interactive visualization showing thedistribution of search interest across the different attributes withrespect to one or more brands in a market.

FIG. 7 illustrates an example interactive visualization of ahead-to-head comparison of search interest between different brands withrespect to each attribute in a list of attributes for a market.

FIG. 8 illustrates an example interactive visualization of the relativeshares of search interest for all brands competing with a given brand ina market.

FIG. 9 illustrates an example interactive visualization of a heat mapshowing the relative similarity between different brands in a marketbased on search interest data and co-occurrences of searches related tothe brands.

FIGS. 10A-10C illustrate an example interface for modeling a real-worldmarket based on search interest data in order to perform market campaignanalysis.

FIG. 11 illustrates an example interactive visualization showing acomparison of different campaigns over different time periods andsources of search interest data.

FIG. 12 conceptually illustrates an example electronic system in whichsome configurations are implemented.

DETAILED DESCRIPTION I. Introduction

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, it will be clear and apparent tothose skilled in the art that the subject technology is not limited tothe specific details set forth herein and may be practiced without thesespecific details. In some instances, well-known structures andcomponents are shown in block diagram form in order to avoid obscuringthe concepts of the subject technology.

The disclosed subject matter relates to providing interactivevisualizations for an entity or group of entities based on searchinterest data derived from Internet or online search trends related tothe entities. Examples of such entities may include, but are not limitedto, stocks or other financial products, companies or businesses, healthconditions or symptoms, movies, celebrities or artists, politicians orpolitical parties, sports teams, etc. In an example, a real-world marketor market segment may be modeled using web search data from search logsthat provide an aggregate record of public search queries conducted overa period of time from many different web users. The web search data maybe processed to discover online search trends or search interest, whichmay be representative of actual consumer interest in a particular brandor product category within a given real-world market. Interactivevisualizations generated from the search interest data may be used byvarious interested parties to gain insights into a particular market ormarket segment. Examples of such interested parties may include, but arenot limited to, professional product marketers or marketing agencies,brand managers or other groups of users who may be interested inobtaining additional information related to different brands within amarket. Thus, a professional marketer or brand manager may use, forexample, interactive visualizations generated from online searchactivity or trends related to particular brands and market attributes toanalyze the effect of different marketing campaigns with respect to agiven market or product category over time.

As will be described in further detail below, different types ofinteractive visualizations may be provided for market research andanalysis so as to enable users (e.g., marketers and advertisers) toleverage search interest data for various business processes. Examplesof such business processes may include, but are not limited to,processes to: (1) learn the structure of a given market; (2) define andmeasure the characteristics of different consumer brands with respect toparticular market segments; (3) present information on market dynamicsfrom different market viewpoints; (4) monitor and create alerts onchanging trends for particular brands based on one or more predeterminedthresholds; (5) analyze relative strengths and user perceptions ofdifferent brands; and (6) benchmark and compare brand and advertising ormarketing campaign performance within a given market across differentindustries. Market research and analysis tools implementing suchinteractive visualizations provide a way to bridge the gap betweenconventional marketing research techniques (e.g., TV campaigns, userspolls and traditional brand metrics) and the modern online marketingarena.

The terms “search interest” and “online interest” are usedinterchangeably herein to refer to a measure of the aggregate popularityof a search term or keyword (e.g., a brand name) among web search users(e.g., in a given location and time period). The aggregate popularity ofa search term may be based on, for example, a relative or normalizedsearch volume returned by a web search engine. The search volume is usedherein to refer to the total count (or number of times) a searchterm/keyword (or synonym thereof) has been entered or queried in thesearch engine during a given period of time. This time period may bemeasured in any of various time increments as desired including, but notlimited to, seconds, minutes, days, months or years. For example, thesearch volume or total count of queries including a particular searchterm occurring within a current time period may be indicative of thecurrent level of search or online interest of that term. While many ofthe examples provided herein are described with respect to a singlesearch engine, it should be noted that the subject technology asdescribed herein may use search query data from multiple search engines.Additionally, while many of the examples provided herein are describedin the context of a market and product brands, it should be noted thatthe subject technology is not intended to be limited thereto and thatthe techniques described herein may be applied to any search topic orcategory of interest based on search queries including one or moresearch terms entered/submitted by different users using a web searchengine.

The term “attribute” may be used herein to refer groups of keywords,which may be predefine or defined by a user (e.g., a brand manager).Multiple attributes may be defined for a given market. For example, auser-defined attribute may include a title that can be used by ananalytics data system as a reference name for identifying the attribute,as will be described in further detail below with respect to FIG. 1.Attributes may also be grouped based on type, which may be useful formanaging lengthy list of attributes. Furthermore, an attribute can beany search interest entity that may represent general user interest,experience, associations, attitudes or sentiments with respect to agiven brand. An attribute may also be used to represent generic productnames or other product-related information, where the attribute mayreference an aggregated set of given keywords.

As noted above, search interest data based on online search trendsrelated to one or more search terms or keywords may be used as a proxyfor user interest with respect to a particular subject or search topicrelated to the search terms. In the context of a consumer market, modernconsumers who are “in the market” to purchase a particular product tendto rely on online search query results from a web search engine as aprimary source of information regarding different products or productbrands of interest. Thus, aggregated search logs representing, forexample, a relatively large volume of search queries submitted by alarge number of different web users over a period of time may be used toextract information relating to the search interest of a particularbrand or other entity in a given market. The search interest for a brandmay provide, for example, a relatively good approximation of overallconsumer interest for the brand in a given real-world market or marketsegment. Furthermore, aggregated search interest data for differentbrands may be integrated into search interest metrics that can be usedto gain market insights into the various interrelations between thebrands and how such interrelations may impact various business decisionsincluding, but not limited to, decisions for advertising, productbranding and positioning strategy, market analysis, etc.

II. Example Network Environment

In an example network environment 100 illustrated in FIG. 1, a datasystem 130 may be used to monitor and track general online activity.Such online activity may include, for example and without limitation,web site traffic associated with one or more public web pages or a websearch engine. In some implementations, data system 130 may beconfigured to process high volumes of public search queries entered orsubmitted by different web users (not shown) via an interface of asearch engine over time. For example, the interface of the web searchengine may be provided by web server 140 as a web page displayed in aweb browser executing at each of the users' respective computingdevices. In operation, each user may interact with various userinterface elements (e.g., a text field) of the search engine interfaceto specify and submit search queries via the web browser. The usersearch queries may be sent by the web browser to web server 140 throughnetwork 120. It should be noted that any online data collected throughsuch interface generally does not include any information that can beused to identify an individual web user. As such, user search queriesare made to be anonymous by, for example, removing any information thatcould be used to identify an individual user. However, the user mayprovide explicit authorization allowing user-specific information (e.g.,demographics of the particular user) related to a search query submittedby the user to be logged with the search query. In some aspects, the website or search engine may allow users to create a personal account orpublic profile in which the user may provide such authorization, e.g.,as part of a privacy setting established for particular user-specifiedcontent or data items associated with the user's account or profile. Assuch, the user may be able to customize the privacy settings fordifferent content in order to grant prior authorization or approval forallowing particular information or content publicly available oridentifiable with the user, while keeping other information private oranonymous.

As illustrated in the example of FIG. 1, data system 130 iscommunicatively coupled to web server 140 and a client device 110 (or“client 110”) via network 120. Network 120 can be any network orcombination of networks that can carry data communication. Such anetwork can include, but is not limited to, a cellular network, a localarea network, medium area network, and/or wide area network such as theInternet, or a combination thereof for communicatively coupling anynumber of mobile clients, fixed clients, and servers. Data system 130includes a server 132, which may be configured to report statisticalinformation based on processed web search traffic data received from webserver 140 or other computing devices through network 120. Client 110may be associated with, for example, a product marketer, brand manager,advertiser or publisher interested in acquiring information related tosearch trends related to consumer products in a particular market (e.g.,market trend information based on search interest data) reported by datasystem 130. Client 110 can be any type of computing device with at leastone processor, local memory, display, and one or more input devices(e.g., a mouse, QWERTY keyboard, touch-screen or microphone). Examplesof different computing devices that may be used to implement client 110include, but are not limited to, a desktop computer, a laptop computer,a handheld computer, a personal digital assistant (PDA), a networkappliance, a mobile handset or smart phone, a tablet computer or acombination of any these data processing devices or other dataprocessing devices. Similarly, each of servers 132 and 140 can beimplemented using any general-purpose computer capable of serving datato client 110 through network 120. Examples of different computingdevices that may be used to implement each of servers 132 and 140include, but are not limited to, a web server, an application server, aproxy server, a network server, or a group of computing devices in aserver farm.

The data system 130 aggregates and reports search query tracking data(also referred to herein as “tracking data”) based on search enginetraffic over a period of time. In some implementations, server 132performs automated segmenting of tracking data included in an eventtracking data communication over a rolling window of time or timeseries. Such tracking data may include, for example and withoutlimitation, a user identifier, an event data, e.g., a timestamp of thecurrent web page event tracking data communication, and user data, e.g.,city or other geographical location of the user. Server 132 may performvarious operations on the event tracking data including, but not limitedto: (1) segmenting the event tracking data according to one or morepredetermined time periods; and (2) storing the tracking data using oneor more table(s) in a database or data store 134 for later retrieval ina database or data store 134 for later retrieval. Server 132 may also beconfigured to perform an additional operation(s) on the segmented data,such as continually sorting the segments of tracking data within each ofthe tables to report the top segments of the tracking data from thosetables. In some implementations, server 132 may be configured to receivethe event tracking data and perform the aforementioned operations inreal-time.

As will be described in further detail below, a marketing agent or brandmanager may provide a list of keywords to be tracked along with othertracking instructions via one or more interfaces provided by data system130 at the brand manager's computing device (e.g., client 110) vianetwork 120. The data that is tracked and collected may be related toone or more different user sessions, during which relevant data iscaptured or logged based on the provided tracking instructions and sentback to server 132132 in the form of an event tracking datacommunication for processing. The event tracking data communication maybe sent, for example, as part of a Hypertext Transfer Protocol (HTTP)request.

In an example of tracking search queries, web server 140 may beconfigured to perform a logging function to log network traffic relatedto relevant search queries submitted by users via the web search engine.Further, data system 130 may be configured to sample and aggregate websearch queries from search logs returned by the web server 140.Additionally, data system 130 may aggregate search data from multiplesources including, for example, multiple web search engines. Theaggregated search data then may be processed by data system 130 in orderto determine the frequency or total number of times a particular term orkeyword (or synonym thereof) is entered or queried by web users ingeneral (e.g., the search volume of the term) during a predeterminedtime period. As described above, this time period may be measured in anyof various time increments as desired including, but not limited to,seconds, minutes, days, months or years.

In some implementations, data system 130 may use the determined searchfrequency of a term/keyword during the predetermined time period toestimate the overall search interest for the term among users in generalduring that time period. Further, data system 130 may be configured toidentify “co-occurrences” of different search queries within apredetermined time proximity of one another. As will be described infurther detail below, some of these search queries may be for one ormore search terms or keywords associated with a particular entity ofinterest and the other query may be for an attribute associated withthat entity or category/type of entity. Searches that co-occur within apredetermined time proximity or relatively period of short time (e.g.,few seconds) of each other may be referred to herein as sharing a searchcontext (or “context”) or as occurring within the same user session (or“mini-session”). A session or mini-session may correspond to, forexample, a set of search queries submitted by a user in the samecontext. The start of a mini-session may coincide with, for example, theinput of a first search query by the user. In some implementations, aheuristic algorithm may be used to determine the end of the mini-sessionas a function of the respective content and time difference betweenconsecutive queries entered by the same user. Search data for suchco-occurrences or “co-searches” (e.g., different searches sharing thesame context) may provide an aggregate representation of users' searchinterest with respect to different entities (e.g., brands in a consumermarket) and their corresponding attributes (e.g., brand or productattributes). For example, in the context of a consumer market, this datamay be useful in conveying information regarding the competitivelandscape of different brands in a given market. Further, the data mayprovide some indication of general user preferences or potentialconsumer trends related to the purchase of products or services withrespect to the various brands in the market.

In addition to the time period or range, other types of data filters maybe applied to the search interest data in order to extract searchinterest metrics for a target population or segment of the market. Assuch, the choice of filters help to define the target population ormarket segment. Examples of other data filters may include, but are notlimited to, search term categories that may represent different verticalmarkets (e.g., automotive, entertainment, food and drink, etc.),geographic regions or locations (e.g., countries, states, cities orother designated geographic areas) and search query properties definingthe source(s) of information to be searched. Examples of differentsources of information may include, but are not limited to, images orvideos in an online media file repository, news sources and productdatabases as well as discussion forums, blogs, information related toone or more social networking sites or services and other user contentsources. As will be described in further detail below with respect toFIGS. 2A-2C, the relevant time period and other data filters may bedefined by a user via an interface provided by data system 130.

Data system 130 in combination with web server 140 may be implementedas, for example, a multi-tiered real-time analytics system formonitoring and reporting web search traffic data in the form of eventtracking data communications, as described above. Such a multi-tieredreal-time analytics system may include different processing tiers forimplementing various functionality within the system. Examples of thevarious functions that may be performed by different processing tiersmay include, but are not limited to, collecting and logging search data,storing the data in persistent storage and processing the stored datafor real-time analytics. Although the processing tiers of such amulti-tiered real-time analytics system are described herein using datasystem 130 and web server 140, each of the aforementioned tiers may beimplemented using a cluster of servers/computers that perform a same setof functions in a distributed and/or load balanced manner. A cluster canbe understood as a group of servers/computers that are linked togetherto seamlessly perform the same set of functions, which can provideperformance, reliability and availability advantages over a singleserver/computer architecture.

In an example implementation of a multi-tiered real-time analyticssystem, web server 140 may be used to implement the aforementioned datacollection and logging tiers of the system. For example, the receiveddata communications based on user search queries may be collected at webserver 140 and routed to data system 130 for persistent and temporarystorage at data store 134 to enable the real-time analytics processingby server 132 or other servers in the system. The operations performedby data system 130 may include reporting (e.g., in real-time) searchinterest data for a particular market according to one or morepredetermined criteria. As will be described in further detail below,the reporting functionality of data system 130 may include providingvarious interactive visualizations based on a structured search marketdefined in part by a list of entities of interest (e.g., competingbrands) within a given market in addition to a list of search interestattributes for the entities.

III. Example Method for Brand and Campaign Analysis

FIG. 2 is a process flowchart of an example method 200 for providinginteractive visualizations for analysis of different brands in a givenmarket or analysis of different advertising or marketing campaignsrelated to the market or segment of the market. As described above, thevisualizations may be based on aggregated search interest dataassociated with a structured search market, as noted above. For purposesof discussion, method 200 will be described using network environment100 (including client 110 and data system 130) of FIG. 1, as describedabove. However, method 200 is not intended to be limited thereto. Forexample, method 200 may be implemented in data system 130 of FIG. 1 forprocessing search interest data as part of a structured search market orpredefined market model and providing interactive visualizations basedon the processing, as will be described in further detail below.Further, the interactive visualizations may be presented to, forexample, a brand manager associated with client 110 of FIG. 1 forfacilitating market research including brand and campaign analysis.

As shown by the example in FIG. 2, method 200 begins in step 202, whichincludes sampling and aggregating user search queries from search logs.As described above, the search logs may be aggregated by an analyticsdata system (e.g., data system 130 of FIG. 1, as described above). Thesearch log may include, for example, a large number of search queriesentered by various users into a web search engine. For example, aninterface of the web search engine may be provided by a web server(e.g., web server 140 of FIG. 1) of the data system over a network(e.g., network 120). As such, the data system may monitor online searchactivity based on user search queries submitted via an interface of theweb search engine or one or more other web search engines. The searchdata may be stored in a data repository or data store (e.g., data store134 of FIG. 1) accessible to the data system.

The aggregated search query data (e.g., stored within the aforementioneddata store) is used in step 204 to compute the frequency at which userssearch for given entities in addition to relevant attributes associatedwith the entities. Each entity may be represented by one or more listsof search terms. Additional lists of search terms may be used torepresent the attributes for the entities. In some implementations, therespective lists for the entities and attributes may be pre-populatedusing a predefined set of search terms or keywords based on, forexample, a category associated with the particular entities.Additionally or alternatively, the search terms for each list may bedefined by the user (e.g., marketer or brand manager) via, for example,an interface (e.g., interface 300 of FIGS. 3A-3C or interface 1000A-C ofFIGS. 10A-10C, as will be described further below), which may beprovided by the data system. The user may also define the category forthe entity via the interface. In some implementations, separateinterfaces may be provided for brand analysis (e.g., interface 300 ofFIGS. 3A-3C) and for campaign analysis (e.g., interface 1000A-C of FIGS.10A-10C), as will be described in further detail below.

As described above, the list of search terms may represent an entity(e.g., a product brand) within a given market while a second list ofterms may represent relevant attributes associated with the marketentities. Examples of such entities may include, but are not limited to,stocks or other financial products, health conditions or symptoms,movies, celebrities or artists, politicians or political parties, sportsteams, or other types of entities that may be represented using a set ofsearch terms or keywords from user search queries. In the context of aconsumer market for products or services, an entity may be a brandassociated with a product or service in a given market. For example, thelist of entities may be a list of brand names identified as majorcompetitors in the given market. a predetermined number of the topbrands competing for business within the given market. The topcompetitors in such a market may be determined according to, forexample, market share or brand popularity. Further, each brand in thelist of brands may correspond to a set of search terms, where each setmay include, for example, well-known or popular variations (e.g.,including any known or frequent misspellings) of the brand name. Ifapplicable, the various competitors may be categorized into apredetermined number of market categories (e.g., Manufactures, OnlineVendors, Retail Chains, etc.). In some other markets, brands can becategorized as Domestic, Imported, etc., if such categorization would beappropriate. It should be noted that brand categories may be defined asdesired by the user (e.g., brand manager). Thus, the list of competingbrands can be defined according to, for example, a specific marketinguse case or based on a specific brand.

The second list of attributes in this example may include a list ofrelevant search terms for the given market. Like the first list, thesearch terms in the second list may be grouped into different sets ofterms corresponding to popular or common variations of the sameattribute. The length or granularity of each list may be adjusted asdesired by the user (e.g., brand manager). In an example, the attributesmay be defined by the user as groups of keywords. As described above,one or more of the attributes may share the same context with a givenbrand in an underlying market. For example, it may be determined that asearch query including one or more search terms corresponding to a brandattribute shares a context with a search query corresponding to thebrand (e.g., search of the brand name) based on whether these searchesoccur (or “co-occur”) within a predetermined period of time (e.g.,within a relatively short time proximity of one another).

The search frequencies calculated in step 204 can be used in step 206,which includes estimating an aggregate web or online user searchinterest in the given entities and attributes. In some implementations,a statistical algorithm may be used to calculate probabilities and jointprobabilities of occurrences of the respective search terms for theentities and attributes. The calculated probabilities may then be usedto estimate users' search interest in general with respect to the givenentities and attributes. Further, the estimated search interest based onthe search terms may be restricted by geographic location and/or timeframe. In the consumer market example, as described above, searchinterest data may be determined based on, for example, probabilities forestimating a likelihood of a search for a particular brand or brand namein a given market segment co-occurring with a search for a one or morerelevant brand attributes (e.g., restricted by a given location and/ortime frame). The search interest data also may be based on a likelihoodof a search for a particular attribute in a given market segmentco-occurring with a search for a particular brand or brand name.

Method 200 then proceeds to step 208, which includes formulatingsimilarity/distance metrics based on the probabilities calculated aspart of the search interest estimation in step 206. The formulatedsimilarity metrics may represent the relative similarity betweendifferent entities in the given market, and thus, may be used as a proxyfor relative similarities between the corresponding real-world entities.Further, search interest metrics derived from aggregated search logs maybe used to model a real-world market, including the various entitieswithin a real-world market, as described above. For example, aggregatedsearch interest information with respect to various brands (e.g., in agiven geographic location and time period) may be extracted andintegrated into a set of brand metrics. The brand metrics for a givenmarket may encapsulate interrelations between different brands in themarket and thus, be used for general market analysis and research aswell as an input for different business decision making processesrelated to, for example, developing targeted advertising or marketingcampaigns as well as branding and positioning strategies. Examples ofvarious metrics that may be calculated for all given brands may include,but are not limited to, mean search volume, volume growth, increases insearch volume for different time frames, volume volatility, trends,seasonality, sudden volume spikes (e.g., corresponding to one or moreoutlier events) and geographical spread. Examples of search interestmetrics will be described in further detail below using a number ofequations or formulas for determining search probabilities based ondifferent interrelations between various entities and/or attributes.However, it is noted that such equations/formulas are provided by way ofexample only and that the subject technology and techniques describedherein are not intended to be limited thereto. Accordingly, any numberof additional equations/formulas may be used as desired to calculatedifferent search probabilities and interrelations between the variousentities or attributes.

In some implementations, the similarity/distance metric is based on anoverlap coefficient for determining a measure of similarity between twosets of search query data. The search query data in each set maycorrespond to, for example, search queries related to different productbrands or brand attributes in a consumer market, as described above.Such a metric may be used, for example, to capture relevant associationsbetween different search keywords (e.g., representing different brandsor attributes) based on co-search data derived from aggregated usersearch queries. In an example, the distance or overlap between twobrands (“brandA” and “brandB”) in a given market may be defined in termsof search probability using equation (1) below:

$\begin{matrix}{1 - \frac{p\left( {{brandA},{brandB}} \right)}{\min\left\{ {{p({brandA})},{p({brandB})}} \right\}}} & (1)\end{matrix}$where p(brandA, brandB) represents the co-search probability for brandAand brandB within the same search query. user session or mini-session,and min{p(brandA}, p(brandB)} represents the smaller of the two brands(or the brand having a relatively lower search probability). Thedistance metric derived from equation (1) in this example may be used todetermine the portion of overlap between brands having different searchvolumes (e.g., small-scale vs. large), which may represent, for example,each brand's total market share or popularity in the given market.

In a further example, the market share of a brand may be defined asfollows:

$\begin{matrix}\frac{p({brandB})}{p({anybrand})} & (2)\end{matrix}$

The probability of the brand co-occurring in a search query with anyother brand in the market may be defined as follows:p(anyNonBbrand|brandB)  (3)

The volume of co-searches for a specific brand (e.g., brandB) out of thevolume of co-searches of any of the brands may be defined as follows:p(brandA|brandB,anyNonBbrand)  (4)

Further, the relative market share of the brand, when co-searched with aspecific attribute X may be defined as follows:

$\begin{matrix}\frac{p\left( {{brandB},{attributeX}} \right)}{p\left( {{anybrand},{attributeX}} \right)} & (5)\end{matrix}$

Similar equations to those provided above may be used to determinesimilar search probabilities for particular attributes. For example, therelative share of the attribute within any search market (e.g.,co-searched with any of the brands) may be defined as follows:

$\begin{matrix}\frac{p\left( {{attributeX},{anybrand}} \right)}{p\left( {{anyattribute},{anybrand}} \right)} & (6)\end{matrix}$

In an example, the search interest metrics may be used by a brandmanager to discover a current trend in search interest for a brand,which may be included in a new promotional marketing campaign. Thesearch interest trend related to the brand then may be compared withother trends during a predetermined time period relative to a baseline.The baseline may be established, for example, in terms of competingbrands or with respect to the general market associated with the brand.Accordingly, new marketing campaigns may be devised or the existingcampaign may be reformulated to account for any changing trends.

In step 210, different normalizations may be applied to the searchinterest and similarity metrics so as to provide, in step 212, differentinteractive visualizations based on the applied normalizations. Theinteractive visualizations may be provided for a given group of brandswithin a market segment. Further, the interactive visualizations may beused to show the interrelations and associations between relevantattributes within a given set of attributes for the brands. As such, thetechniques utilized for such normalizations may be based on, forexample, visualization requirements related to presenting a cross marketview for comparing different brands in a given market or otherrequirements related to the market data analyses and visualizations tobe provided in step 212 to the user. Examples of different interactivevisualizations may include, but are not limited to, multi-dimensionalcolor presentations, various types of charts (e.g., bar graphs, bubblecharts or stacked charts), heat maps. As will be described in furtherdetail below, such interactive visualizations may be presented inmultiple user-controlled dimensions based on one or more statisticalanalysis techniques including, but not limited to, Multi-DimensionalScaling (MDS) and Clustering Analysis.

IV. Example Interfaces for Market Analysis Based on Search Interest Data

Initially, an example interface for defining a structured search marketfor modeling a given real-world market for purposes of brand analysiswill be described with respect to FIGS. 3A-3C. In particular, FIGS.3A-3C show an example implementation of an interface 300 that allows auser (e.g., a product marketer or brand manager) to define a structuredsearch market (or “market model”) that can be used to model a givenreal-world market or market segment for purposes of brand analysis, asdescribed above. For purposes of discussion, interface 300 of FIGS.3A-3C and the example visualizations of FIGS. 4A-4D, 5A-5C and 6-9 willbe described in the context of, for example, the brand analysis portionof a set of tools for performing brand and campaign analysis andresearch for a given market or market segment.

Next, interface 1000A-C of FIGS. 10A-10C and the example visualizationof FIG. 11 will be described in the context of the campaign analysisportion of these tools. For example, interface 300 and interface 1000A-Cmay be included as different interfaces within a set of market analysistools. Further, interface 300 or interface 1000A-C may be used as partof the analysis toolset to define one or more market models usingrelevant keywords and to generate either standard or customized reports,including appropriate visualizations, for brand analysis or campaignanalysis, respectively. In some implementations, interface 300 mayinclude different user interface controls (not shown) for enabling auser, e.g., a brand manager, to switch between different tools forcampaign analysis and brand analysis.

It should be noted that the example interfaces and interactivevisualizations for brand analysis and campaign analysis, as noted aboveand as will be described in further detail below, are provided forillustrative purposes only, and that the subject technology describedherein are not intended to be limited thereto.

V. Defining a Structured Search Market Model for Brand Analysis

Interface 300 includes various control elements within a portion of theinterface (e.g., in an options or settings pane of a market modelediting window, as shown in FIG. 3A) that a marketer or brand managercan use to input different parameters associated with a structuredsearch market. In particular, the market model in this example may bedefined primarily by search terms or keywords corresponding to a list ofbrands and a list of relevant brand attributes, as described above. Forexample, a brand manager may use interface 300 to create a new marketmodel or make changes to an existing market model by selecting controlbutton 302A. Control button 302A may be implemented, for example, as alist control element that can be selected and expanded to display a listof previously created market models. The brand manager can define a namefor a newly market model by entering the name (e.g., in text format)into an input field 304A. For example, FIG. 3C shows a version of aportion of interface 300 in which the name “US Automotive” is specifiedfor the market model via input field 304C. A control button 350A ofinterface 300 enables the brand manager to save a newly created marketmodel with the specified name for later use. In the example interface300 illustrated in FIG. 3A, the search terms or keywords for the list ofbrands and list of attributes may be defined by the brand manager via aninput field 306A and an input field 308A, respectively. Input fields306A and 308A may be implemented as, for example, text boxes allowingthe brand manager to manually enter the search term/keywords via a userinput device (e.g., keyboard) coupled to the computing device at whichthe interface is being provided.

In some implementations, interface 300 may provide predefined lists ofbrands and brand attributes for each of various market categories. Theparticular market categories provided by interface 300 may correspondto, for example, a predetermined number of popular vertical markets. Thepopular vertical market categories may have been previously identifiedor predetermined by, for example, analyzing and grouping popular orfrequently submitted/entered user search queries into identifiablemarket categories. As shown in FIG. 3A, interface 300 may include a listcontrol element 310A enabling the brand manager to select a marketcategory from a predefined list of available market categories or searchfor a new market category via interface 300. For example, selection oflist control element 310A may allow the brand manager to view anexpanded list (e.g., expanded list control element 310B, as shown inFIG. 3B) or a pop-up window including the different market categories.The brand manager may define the market category for the new marketmodel being created by selecting a market category as listed in anexpanded list control element 310B, as shown in FIG. 3B. The expandedlist control element 310B shown in FIG. 3B may be, for example, anexpanded version of control element 310A of FIG. 3A. Additionally, thebrand manager may search for additional market categories by entering aterm or keyword related to a particular market or market category intoan input field 312B of expanded list control element 310B. As shown inFIG. 3B, input field 312B includes a search button 314B that allows thebrand manager to initiate a search for additional market categoriesbased on the term or keyword entered into input field 312B.

In some implementations, selecting a market category from expanded listcontrol element 310B automatically defines the market model beingcreated via interface 300 with relevant search terms/keywords or otherparameter values based on the selected market category. Further,interface 300 may be configured to automatically populate the userfields or control elements defining the parameters of the market modelbeing created. In the example shown in FIG. 3B, selecting the “Autos &Vehicles” market category listed within the expanded list controlelement 310B may cause interface 300 to automatically populate each ofinput fields 306B and 308B with search terms/keywords from a predefinedlist of brands in addition to a predefined list of attributes,respectively, related to the particular market category. For example,FIG. 3C shows interface 300 in which the list of brands and the list ofattributes are each pre-populated with a predefined set of search termsor keywords. The predefined list of brands may include, for example, apredetermined number of the top brands competing in the given market.The top brands may be identified based on, for example, the respectivemarket share of each brand (e.g., based on revenue figures released bythe manufacturer/provider of the products or services associated witheach brand). Examples of keywords for brand attributes may include, butare not limited to, “Parts,” “sports utility vehicle” (or “SUV”),“Performance,” “Safety,” “Engine,” “Hybrid,” “Trade-In,”“Leasing,”“anti-lock braking system” (or “ABS”), and other terms related to theautomotive market or vehicles in general.

The number of brands to include in the list may be based on, forexample, a level of granularity desired by the brand manager. Further,interface 300 may enable the brand manager to add other search terms orkeywords (e.g., additional brand names) to the predefined list of brandsor predefined list of attributes populated within input fields 306B and308B. The ability to add keywords related to the market category maytherefore allow the brand manager to further define the market model(e.g., with a higher degree of granularity). Although the examplesillustrated in FIGS. 3A-3C and other examples provided herein may bedescribed with respect to an automotive market or market category, thetechniques described herein are not intended to be limited thereto andmay be applied to any market or other relevant search interest category.

In addition to the above-described brand-related lists of search termsand market category, the brand manager in this example may use interface300 to define additional parameters for the market model, which may beused as additional data filters for the search data, as describedpreviously. Thus, interface 300 includes list control elements 320A,330A, 335A and 340A, each of which correspond to a different search datafilter or parameter that may be defined for the market model. While onlylist control elements 320A, 330A, 335A and 340A are shown, it should benoted that additional control elements corresponding to additional datafilters or market model parameters may be used as desired. Like listcontrol element 310A, the brand manager may select (e.g., via a userinput device) any of these list control elements within interface 300 inorder to view an expanded list including various options that can beselected for defining each market model parameter. In the example shownin FIG. 3A, the aforementioned list control elements may be used by thebrand manager to define the following market model parameters/filters: ageographic region or location, e.g., country, state, province, city orother designated geographic territory or area of interest (using listcontrol element 320A); a search channel or source of search data to beaggregated and processed, e.g., images or videos in an online media filerepository, news sources, product databases (using list control element340A); a time increment for specifying the level of granularity orlength of the time period for which the market analysis is to beperformed (using list control element 330A); and a starting time period(using list control element 335A) that is based on the defined periodlength or time granularity (e.g., as selected via list control element330A). The relevant time period options listed for list control element330A may include, for example, quarterly, semi-annual or annual.Accordingly, the options for starting time period as listed in anexpanded view of list control element 335A may be updated based on theselected time period length in list control element 330A.

Referring back to FIG. 1, interface 300 of FIGS. 3A-3C may be providedby, for example, data system 130 of network environment 100, asdescribed above. In some implementations, the interface may be providedto a marketer or brand manager associated with client 110 via astandalone application executing at client 110. The interface also maybe provided by data system 130 as, for example, a web service overnetwork 120. The web service may be accessible through, for example, oneor more web pages loaded within a web browser executable at client 110or other device (not shown) associated with the marketer/brand manager.In some implementations, a portion of the interface 300 or otherinterface may be used to present various interactive visualizations tothe brand manager. For example, data system 130 may provide suchvisualizations using different market or brand analysis techniques thatmay be applied to search interest data associated with a structuredsearch market or market model, e.g., created using interface 300, asdescribed above with respect to FIGS. 3A-3C.

VI. Interactive Visualizations for Brand Analysis

FIGS. 4A-4D, 5A-5B and 6-9 (“FIGS. 4A-9”) illustrate exampleimplementations of various interactive visualizations of search interestdata for brand analysis with respect to different brands in a market.Although the example visualizations of FIGS. 4A-9 will be described inthe context of different interfaces, it should be noted that theseexample visualizations may be provided within a single interface (e.g.,as different windows or pages). For example, the interactivevisualizations may be presented to a brand manager via, for example,interface 300 of FIGS. 3A-3C, as described above, at a computing device(e.g., client 110 of FIG. 1, as described above) associated with thebrand manager. For example, the interactive visualizations may be usedby the brand manager to discover market insights or gain a betterunderstanding of a given market and its dynamics. For purposes ofexplanation, FIGS. 4A-9 will be described using network environment 100of FIG. 1 and interface 300 of FIGS. 3A-3C, as described above, but theinteractive visualizations of FIGS. 4A-9 are not intended to be limitedthereto. Thus, for example, the interactive visualizations of FIGS. 4A-9may be generated based on the structured search market or market modelcreated using interface 300 for brands in an automotive market, asdescribed above. Like the examples illustrated in FIGS. 3A-3C, FIGS.4A-9 are not intended to be limited thereto, and thus, may be applicableto any market or category of search interest data.

A. Example Visualization: Brands Map

FIGS. 4A-4D illustrate different views of an interface 400 for anexample interactive visualization, in which a brands map representingdifferent automotive brands in a real-world automotive market isprovided. Using the example market model described above with respect toFIGS. 3A-3C, the brands in the example brands map visualization of FIGS.4A-4D may be associated with the “Autos & Vehicles” market category, asshown in FIG. 3B and described above. The visualized brands map mayinclude, for example, automotive brand names from a pre-defined list ofthe major automotive brands in the automotive industry or market (e.g.,the list of brands displayed within input field 308C of interface 300,as shown in FIG. 3C and described above).

For example, the brands map may be used to provide different views of anassociations matrix between the different brands and/or brandattributes. In some implementations, a two-dimensional representation ofthe brands map may include circle shapes and a three-dimensionalrepresentation of the brands map may include spherical shapes or bubblesrepresenting the brands. As shown in the example of FIG. 4A, the brandsmap may be visualized in a content area 430A of interface 400 as abubble map, where each of the relevant brands are represented by acircle or bubble shape. Further, interface 400 may include a control404A (e.g., a slider control) allowing a user to change a zoom level atwhich the brands map is displayed in the content area. For example, FIG.4B shows a view of the brands map at a higher zoom level in a contentarea 430B after the user changed a position of control 404A to adifferent position corresponding to control 404B (e.g., moved the slidercontrol in a direction that increases the zoom level). The user mayinteract with interface 400, including control 404A/404B and othercontrol elements, using any of various user input devices (e.g., mouseor touch-screen), as described above with respect to client 110 of FIG.1.

The relative positions of the brands (or bubbles shapes representing thebrands) in the brands map in FIG. 4A, particularly with respect to thedistances between different brands in the brands map as visualized incontent area 430A, may be based on similarity metrics formulated for thebrands based on search interest data associated with the automotivemarket model, as described above with respect to method 200 of FIG. 2.Further, one or more statistical analysis techniques including, but notlimited to, Multi-Dimensional Scaling (MDS) or Clustering Analysis maybe used to generate the brands map. As described above, the similarityor distance metrics may represent a search-based distance betweendifferent brands in an automotive market model. For example, thesimilarity or distance relation between brands may be based on aprobability or likelihood of co-occurrences of searches for therespective brands at adjacent times (e.g., occurring in adjacent usersearches within a relatively short time proximity). Thus, users may bemore likely to search for any two brands having a greater similarityrelation (or less distance) between them in the same search session oreven in the same search query, in comparison with a combination of twobrands that are located farther away from each other in the brands map.Further, there may be a greater chance that brands appearing closer indistance relative to other brands represented on the brands map arecompetitors in the same user context. FIG. 4A shows an exampleexpression 402 that may be used in determining the similar/distancerelation between brands.

As shown by the example in FIG. 4A, interface 400 may include a list orselector control 410A and a selector control 420A. Selector control 410Amay be used to select a particular brand to emphasize within the brandsmap as visualized in content area 430A. FIG. 4C shows an example inwhich the user has selected a particular brand in the brands map toemphasize relative to others. As shown in FIG. 4C, the user may selectthe brand from a list of brands displayed in a list box (or expandedlist control) 412C, after selecting the selector control 410C. Theemphasized brand may be represented in any of various ways so as todistinguish the brand from the other brands displayed in the brands map.For example, the emphasized brand may be represented with adifferent-colored bubble than the bubbles for the other brands, as shownby bubble 432C in content area 430C of FIG. 4C. In some implementations,different color variations may be used in the visualized brands map todistinguish brands based on, for example, a brand category (e.g., brandsin different brand categories are displayed in different colors) orbased on whether the brand is from a predefined list of brands or brandcategories or was defined by the user.

Returning to FIG. 4A, the size of the bubbles depicted for the brands inthe brands map may be changed using selector control 420A. The size ofeach bubble may represent, for example, a scaled search volume for thecorresponding brand in the automotive market model. Thus, the visualizedbrand map enables the user to compare relative search volumes among thedifferent brands. For example, the user may determine which of thebrands has the greatest relative search volume (e.g., “Ford”) or has theleast search volume (e.g., “Ferrari”) by viewing the visualized brandsmap in content area 430A. The user may also have the option to view thebrands map without taking search volume into account. As shown in theexample of FIG. 4D, the user may choose to ignore volume for the brandsrepresented in the visualized brands map by selecting the appropriateoption in list box 422D of selector control 420D. The bubbles for thebrands in the brands map as displayed in content area 430D are adjustedaccordingly. Although the bubble sizes in the visualized brands map ofthe example interface 400 of FIGS. 4A-4D have been described asrepresenting search volume, it should be noted that in other example,the bubble sizes may be used to represent other aspects of the searchdata for the list of brands in the market model. In someimplementations, the bubble size may represent a level of searchinterest for the brands (e.g., across all search categories).

B. Example Visualization: Brands Share

FIGS. 5A-5C show different views of an interface 500 for another exampleinteractive visualization for the automotive market model describedabove. As shown in FIG. 5A, the example visualization may be used toshow how overall search interest with respect to the entire market(e.g., including all attributes) is distributed across the differentbrands competing in the market. The share of search interest for thebrands may be visualized as a bar graph, where the height of each bar inthe bar graph represents the relative share of search interest for eachcompetitor brand with respect to all attributes in the market as awhole. The relative share may be represented as, for example, thefraction of search interest for a brand given that a search for anyrelevant attribute associated with the market has occurred relative toall the brands in the market. Also, as shown in FIG. 5A, interface 500may include a list or selector control 510A allowing the user to selecta particular attribute from the list of attributes defining the marketmodel in this example. For example, as shown in FIG. 5B, selectorcontrol 510B may be expanded to display a list of attributes within alist box 512B. The user may select an attribute of interest from thedisplayed list of attributes. Accordingly, FIG. 5C shows another view ofinterface 500 in which the visualized brands share graph has beenupdated to reflect the user's selection of the attribute. Also, as shownin FIG. 5C, interface 500 may enable the user to view a numerical valuefor the relative share of a particular brand (e.g., in a text box orpop-up window 502C) by selecting (using a user input device) the bar inthe bar graph corresponding to the particular brand.

C. Example Visualization: Attributes Share

FIG. 6 shows an interface 600 for another example interactivevisualization showing the distribution of search interest across thedifferent attributes with respect to one or more brands in the market.Similar to the example visualization of brands share, as shown in FIGS.5A-5C and described above, the share of search interest for theattributes may be visualized as a bar graph. However, the height of eachbar in the bar graph represents the relative share of search interestfor each attribute with respect to either all brands or a particularbran in the market. The relative attribute shares may be represented as,for example, the fraction of search interest for an attribute given thata search for one or more brands within the market has occurred. Also,interface 600 for the attributes share visualization may provide similarfunctionality as that of the brands share visualization in the previousexample of FIGS. 5A-5C, as described above. In the example shown in FIG.6, interface 600 may allow the user to select a brand of interest from alist of brands displayed in a list box 612 associated with a selectorcontrol 610.

D. Example Visualization: Head-to-Head Brand Comparison

FIG. 7 shows an interface 700 for another example interactivevisualization, which shows a head-to-head comparison of two differentbrands with respect to all attributes. A benefit of such an interactivevisualization includes enabling the user (e.g., brand manager) toidentify specific attributes as the key sources of competition betweendifferent competing brands. In addition, such an interactivevisualization enables the user to identify the relative share of thesekey attributes with respect to search interest in a given market ormarket segment. As shown in the example of FIG. 7, interface 700includes selector controls 710 and 720 for enabling the user to specifyeach of the brands to compare. Although, the example visualization ofFIG. 7 is shown as a horizontal bar graph, the interactive visualizationfor head-to-head brand comparisons may be implemented using other typesof graphs (e.g., scatter plot) as may be desired. Like the previousexample visualizations, interface 700 may enable the user to interactwith the visualization to view additional information with respect to,for example, a particular brand. As shown in FIG. 7, interface 700 maybe configured to display a pop-up window 702 that shows numerical valuesfor the respective interest shares of the brands being compared withrespect to an attribute by selecting a portion of the visualizationcorresponding to that attribute.

E. Example Visualization: Top Competitors

FIG. 8 shows an interface 800 for another example interactivevisualization, which shows a chart (e.g., bar graph) of the relativeshares of search interest for all brands competing with a given brand inthe market. As shown in FIG. 8, a particular brand of interest to theuser may be selected using a selector control 810 (e.g., from a list ofbrands that may be displayed in association with selector control 810).Once selected, the bar graph will be visualized to show the relativeinterest shares distributed among the different brands competing withthe user-selected brand. In some implementations, the bar heights of thebar graph in the example may be scaled such that the competitor brandhaving the greatest relative share is assigned a predetermined maximumshare value (e.g., 100%), and the other competing brands are scaledaccordingly.

F. Example Visualization: Brands Heat Map

FIG. 9 shows an interface 900 for another example interactivevisualization of a brands heat map. As shown in the example of FIG. 9,the visualized brands heat map includes a mapping of the relativesimilarity of each brand in comparison with all other brands in themarket (e.g., in the list of predefined brands for the automotive marketmodel, as described above). Also, as shown in FIG. 9, the brands arelisted across both X and Y axes of the heat map, and a percentagerepresenting the relative similarity between any two brands in themarket is provided at each intersection point of the map. A percentagevalue of 100% denotes an intersection of the same brand. The percentagemay be calculated based on, for example, the similarity or distancerelation between brands, as described above. For example, the percentagemay represent a probability or likelihood of co-occurrences of searchesfor the respective brands at adjacent times (e.g., occurring in adjacentuser searches within a relatively short time proximity). Like theexample brands map visualization of FIGS. 4A-4D described above, thevisualized heat map of FIG. 9 may be based on an expression 902 thatrepresents the similarity/distance relation between any brand (‘A’) andany other brand (‘B’). In some implementations, the heat map may bedisplayed using a range of different colors (or different shades of thesame color) to represent the degree of similarity between differentbrands based on their respective similarity/distance percentages. Theorder of the rows or columns in the heat map can be chosen based on, forexample, output scores generated using any of various data analysistechniques including, but not limited to, Principle Components Analysisor Clustering Analysis. In this way, the visualized heat map can be usedto demonstrate the structure of the given market. In the example heatmap as shown in FIG. 9, the order of the rows and columns were selectedso as to follow the first axis of the example brand map as shown inFIGS. 4A-4D, as described above, thereby preserving the same marketclusters as shown in the brand map.

VII. Defining a Campaign for Campaign Analysis

FIGS. 10A-10C illustrate different screens or pages of an exampleinterface (collectively referred to as “1000A-C”) for modeling areal-world market based on search interest data in order to performmarket campaign analysis. In some implementations, interface 1000A-C maybe included along with interface 300 in a set of marketing analysistools to enable a brand manager to switch between each interface byselecting different user interface controls (not shown) for eithercampaign analysis or brand analysis. As shown by the example in FIGS.10A-10C, interface 1000A-C may be used to create a new campaign (e.g.,new advertising or marketing campaign). For example, a brand manager mayuse the first page of the interface 1000A, as shown in FIG. 1000A, toindicate a name for the campaign, geographic location and a time periodfor introducing a new campaign. The second page of the interface, asshown in FIG. 10B, may be used to define the relevant keywords for thecampaign. In an example, the brand manager can specify that the campaignbe introduced if a level of web search interest for a particular term(e.g., brand) or groupings of terms (e.g., brand attribute) exceeds apredefined threshold. Further, the brand manager may specify thatcertain campaigns be used only in particular geographic markets orduring certain time periods.

As described above, search interest metrics may be used to discovercurrent trends with respect to a marketing campaign. For example, thesearch interest trend related to a brand that is the subject of a newpromotional advertising/marketing campaign may be compared with othertrends during a predetermined time period relative to a baseline. Thebaseline may be established, for example, in terms of a previouscampaign during an initial or previous time period. For example, theinitial or previous time period may be for the same increment of time,but correspond to a different year with respect to the general market orparticular market category.

VIII. Interactive Visualization for Campaign Analysis

FIG. 11 shows an example visualization for campaign analysis with achart 1120 including a time series that represents a current change oftrend of online search interest for a campaign related to the “computersand electronics” market category during a specified time period in year2011 relative to a previous time period in year 2010. The current changeof trend of online search interest may be determined by calculating adifference between a predetermined trend of online search interestduring the initial or previous time period (e.g., corresponding to a“pre-campaign”) and the current trend of online search interest duringthe specified time period corresponding to the campaign. Additionally, asecond time series representing a change in the general trend of onlinesearch interest for the market category related to the campaign may alsobe provided. Further, the interactive visualization includes a chart1122 showing a comparison of search interest across different sources orchannels of search data including, for example and without limitation,product search queries, web search queries and media search queries.Accordingly, the brand manager may use the visualized information forcampaign analysis in order to assess previous campaigns or introduce newmarketing campaigns based on changing trends.

IX. Example Computer System

FIG. 12 conceptually illustrates an example electronic system 1200 withwhich some implementations of the subject technology are implemented.For example, any of the various computing devices (e.g., client 110,computing system 130 or web server 140) of network environment 100 ofFIG. 1, as described above, may be implemented using electronic system1200. Electronic system 1200 can be a computer, phone, PDA, or any othersort of electronic device. Such an electronic system includes varioustypes of computer readable media and interfaces for various other typesof computer readable media. Electronic system 1200 includes a bus 1208,processing unit(s) 1212, a system memory 1204, a read-only memory (ROM)1210, a permanent storage device 1202, an input device interface 1214,an output device interface 1206, and a network interface 1216.

Bus 1208 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices ofelectronic system 1200. For instance, bus 1208 communicatively connectsprocessing unit(s) 1212 with ROM 1210, system memory 1204, and permanentstorage device 1202.

From these various memory units, processing unit(s) 1212 retrievesinstructions to execute and data to process in order to execute theprocesses of the subject disclosure. The processing unit(s) can be asingle processor or a multi-core processor in different implementations.

ROM 1210 stores static data and instructions that are needed byprocessing unit(s) 1212 and other modules of the electronic system.Permanent storage device 1202, on the other hand, is a read-and-writememory device. This device is a non-volatile memory unit that storesinstructions and data even when electronic system 1200 is off. Someimplementations of the subject disclosure use a mass-storage device(such as a magnetic or optical disk and its corresponding disk drive) aspermanent storage device 1202.

Other implementations use a removable storage device (such as a floppydisk, flash drive, and its corresponding disk drive) as permanentstorage device 1202. Like permanent storage device 1202, system memory1204 is a read-and-write memory device. However, unlike storage device1202, system memory 1204 is a volatile read-and-write memory, such arandom access memory. System memory 1204 stores some of the instructionsand data that the processor needs at runtime. In some implementations,the processes of the subject disclosure are stored in system memory1204, permanent storage device 1202, and/or ROM 1210. For example, thevarious memory units include instructions for providing interactivevisualizations of search interest data in accordance with someimplementations. From these various memory units, processing unit(s)1212 retrieves instructions to execute and data to process in order toexecute the processes of some implementations (e.g., the steps of method200 of FIG. 2, as described above).

Bus 1208 also connects to input and output device interfaces 1214 and1206. Input device interface 1214 enables the user to communicateinformation and select commands to the electronic system. Input devicesused with input device interface 1214 include, for example, alphanumerickeyboards and pointing devices (also called “cursor control devices”).Output device interfaces 1206 enables, for example, the display ofimages generated by the electronic system 1200. Output devices used withoutput device interface 1206 include, for example, printers and displaydevices, such as cathode ray tubes (CRT) or liquid crystal displays(LCD). Some implementations include devices such as a touch-screen thatfunctions as both input and output devices.

Finally, as shown in FIG. 12, bus 1208 also couples electronic system1200 to a network (not shown) through a network interface 1216. In thismanner, the computer can be a part of a network of computers (such as alocal area network (“LAN”), a wide area network (“WAN”), or an Intranet,or a network of networks, such as the Internet. Any or all components ofelectronic system 1200 can be used in conjunction with the subjectdisclosure.

These functions described above can be implemented in digital electroniccircuitry, in computer software, firmware or hardware. The techniquescan be implemented using one or more computer program products.Programmable processors and computers can be included in or packaged asmobile devices. The processes and logic flows can be performed by one ormore programmable processors and by one or more programmable logiccircuitry. General and special purpose computing devices and storagedevices can be interconnected through communication networks.

Some implementations include electronic components, such asmicroprocessors, storage and memory that store computer programinstructions in a machine-readable or computer-readable medium(alternatively referred to as computer-readable storage media,machine-readable media, or machine-readable storage media). Someexamples of such computer-readable media include RAM, ROM, read-onlycompact discs (CD-ROM), recordable compact discs (CD-R), rewritablecompact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM,dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g.,DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SDcards, micro-SD cards, etc.), magnetic and/or solid state hard drives,read-only and recordable Blu-Ray® discs, ultra density optical discs,any other optical or magnetic media, and floppy disks. Thecomputer-readable media can store a computer program that is executableby at least one processing unit and includes sets of instructions forperforming various operations. Examples of computer programs or computercode include machine code, such as is produced by a compiler, and filesincluding higher-level code that are executed by a computer, anelectronic component, or a microprocessor using an interpreter.

X. Conclusion

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, some implementations areperformed by one or more integrated circuits, such as applicationspecific integrated circuits (ASICs) or field programmable gate arrays(FPGAs). In some implementations, such integrated circuits executeinstructions that are stored on the circuit itself.

As used in this specification and any claims of this application, theterms “computer”, “server”, “processor”, and “memory” all refer toelectronic or other technological devices. These terms exclude people orgroups of people. For the purposes of the specification, the termsdisplay or displaying means displaying on an electronic device. As usedin this specification and any claims of this application, the terms“computer readable medium” and “computer readable media” are entirelyrestricted to tangible, physical objects that store information in aform that is readable by a computer. These terms exclude any wirelesssignals, wired download signals, and any other ephemeral signals.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

It is understood that any specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged, or that allillustrated steps be performed. Some of the steps may be performedsimultaneously. For example, in certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. Pronouns in themasculine (e.g., his) include the feminine and neuter gender (e.g., herand its) and vice versa. Headings and subheadings, if any, are used forconvenience only and do not limit the subject disclosure.

A phrase such as an “aspect” does not imply that such aspect isessential to the subject technology or that such aspect applies to allconfigurations of the subject technology. A disclosure relating to anaspect may apply to all configurations, or one or more configurations. Aphrase such as an aspect may refer to one or more aspects and viceversa. A phrase such as a “configuration” does not imply that suchconfiguration is essential to the subject technology or that suchconfiguration applies to all configurations of the subject technology. Adisclosure relating to a configuration may apply to all configurations,or one or more configurations. A phrase such as a configuration mayrefer to one or more configurations and vice versa.

The word “exemplary” is used herein to mean “serving as an example orillustration.” Any aspect or design described herein as “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs.

What is claimed is:
 1. A computer-implemented method comprising:obtaining aggregate search data based on session logs of search queriessubmitted by users to a search engine during a predetermined time periodof network search activity; obtaining a first list of search termscorresponding to a plurality of search entities; determining a searchfrequency for each search entity in the aggregate search data based on acorresponding search term in the first list of search terms; calculatingsearch probabilities for each search entity based on the determinedsearch frequency of the search entity in the aggregate search data, thesearch probabilities indicating a likelihood of search queries relatedto the search entity co-occurring with search queries related to one ormore different search entities; estimating a level of search interestfor each of the plurality of search entities based on the searchprobabilities; generating a search market model for the plurality ofsearch entities based on the estimated level of search interest of eachsearch entity during the predetermined time period; and providing aninteractive visualization showing interrelations between the pluralityof search entities of the generated search market model.
 2. The methodof claim 1, wherein the search engine corresponds to one or more sourcesof online search data and the session logs are obtained from the one ormore sources.
 3. The method of claim 2, wherein the one or more sourcesinclude a web search engine for submitting general online searchqueries, a product search engine for submitting online search queriesfor information related to consumer products or services, and a mediasearch engine for submitting online search queries related to mediacontent.
 4. The method of claim 1, comprising: obtaining a second listof search terms corresponding to attributes of the plurality of searchentities; determining a search frequency for each attribute in theaggregate search data based on a corresponding search term in the secondlist of search terms; calculating joint search probabilities for theplurality of search entities and related attributes based on therespective search frequency determined for each search entity and eachattribute, the search probabilities indicating a likelihood of searchqueries related to each search entity co-occurring with search queriesrelated to each attribute during the predetermined time period;estimating a second level of search interest for each search entityrelative to one or more of the attributes based on the calculated jointsearch probabilities; and generating a new search market model based onthe estimated second level of search interest.
 5. The method of claim 4,wherein the second level of search interest is estimated usingsimilarity metrics based on the joint search probabilities calculatedfor the plurality of search entities.
 6. The method of claim 4, furthercomprising: providing a second interactive visualization showinginterrelations between one or more of the plurality of search entitiesand one or more of the attributes for the new search market model. 7.The method of claim 4, wherein the first and second lists of searchterms are obtained based on user input via an interface of the searchengine.
 8. A non-transitory machine-readable medium comprisinginstructions stored therein, which when executed by a processor, causesthe processor to perform operations comprising: obtaining aggregatesearch data based on session logs of search queries submitted by usersto a search engine during a predetermined time period of network searchactivity; identifying search entities based on a first list of searchterms corresponding to the search entities; identifying attributesrelated to the identified search entities based on a second list ofsearch terms corresponding to the attributes; determining a searchfrequency for each search entity and each attribute in the aggregatesearch data based on corresponding search terms in the respective firstand second lists of search terms; calculating search probabilities forthe search entities and related attributes based on the respectivesearch frequency determined for each search entity and each attribute,the search probabilities indicating a likelihood of search queriesrelated to each search entity co-occurring with search queries relatedto each attribute during the predetermined time period estimating alevel of search interest for each of the search entities relative to oneor more of the attributes based on the calculated search probabilities;generating a search market model for mapping interrelations between thesearch entities and related attributes based on the estimated level ofsearch interest; and providing an interactive visualization of theinterrelations between one or more of the search entities and one ormore of the attributes based on the generated search market model. 9.The machine-readable medium of claim 8, wherein the level of searchinterest is estimated using similarity metrics based on the searchprobabilities calculated for the search entities and related attributes.10. The machine-readable medium of claim 8, wherein the first and secondlists of search terms are obtained based on user input via an interfaceof the search engine.
 11. The machine-readable medium of claim 8,wherein the search engine corresponds to one or more sources of onlinesearch data and the session logs are obtained from the one or moresources.
 12. The machine-readable medium of claim 11, wherein the one ormore sources include a web search engine for submitting general onlinesearch queries, a product search engine for submitting online searchqueries for information related to consumer products or services, and amedia search engine for submitting online search queries related tomedia content.
 13. A computer-implemented method comprising: determininga historic trend of online search interest in a search entity based onsearch queries related to the search entity submitted by users to asearch engine during a first time period, the search entitycorresponding to one or more related search terms submitted as part ofthe related search queries; determining a current trend of online searchinterest in the search entity based on search queries related to thesearch entity submitted during a second time period; and providing aninteractive visualization comparing the historic and current searchtrends of online search interest for the search entity over a timeseries including the first and second time periods; calculating adifference between the historic trend of online search interest for thesearch entity and a general trend of online search interest for one ormore other search entities within a search category associated with thesearch entity during the first time period; calculating a differencebetween the current trend of online search interest determined for thesearch entity and the general trend of online search interest for theone or more other search entities within the search category during thesecond time period; and updating the visualization with a second timeseries representing a change of online search interest trends for thesearch entity relative to the one or more other search entities withinthe search category, based on the calculated differences between thegeneral trend of online search interest for the search category and thehistoric and current trends of online search interest for the searchentity over the second time series, the second time series including thefirst and second time periods.
 14. The method of claim 13, wherein thecurrent and historic trends are determined for a predeterminedgeographic location.
 15. The method of claim 13, wherein the one or morerelated search terms corresponding to the search entity include aplurality of attributes related to the search entity.
 16. The method ofclaim 13, wherein the historic and current trends of online searchinterest are determined based on aggregate search data derived fromsession logs of search queries related to the search entity fromdifferent online users during the respective first and second timeperiods.
 17. A non-transitory computer-readable medium storing softwarecomprising instructions executable by one or more computers which, uponsuch execution, cause the one or more computers to perform operationscomprising: obtaining aggregate search data based on session logs ofsearch queries submitted by users to a search engine during apredetermined time period of network search activity; obtaining a firstlist of search terms corresponding to a plurality of search entities;determining a search frequency for each search entity in the aggregatesearch data based on a corresponding search term in the first list ofsearch terms; calculating search probabilities for each search entitybased on the determined search frequency of the search entity in theaggregate search data, the search probabilities indicating a likelihoodof search queries related to the search entity co-occurring with searchqueries related to one or more different search entities; estimating alevel of search interest for each of the plurality of search entitiesbased on the search probabilities; generating a search market model forthe plurality of search entities based on the estimated level of searchinterest of each search entity during the predetermined time period; andproviding an interactive visualization showing interrelations betweenthe plurality of search entities of the generated search market model.